Discrete event simulation is used to model, study, plan, and improve systems in which random events play a dominant role. These systems are often driven by complicated mathematical and logical relationships, making it difficult to derive an analytical solution. Simulation involves the process of building a model that mimics the behavior of a real-world system of interest.
Often, by using statistical analysis and related methods, it is possible to uncover logical and mathematical relationships among elements of a system. Moreover, many real-world systems include not only complex mathematical and logical relationships but also significant random components. For such systems an analytical model, even a simple one, might not be possible. A far better approach is to incorporate the random elements of the system in the model. Discrete event simulation is one such modeling technique.
A prime motivation for building and running a discrete event simulation model is the creation of realistic data on the performance of the system being modeled, with an overall goal of using the data to make statistically valid inferences about the performance of the system. Discrete event simulation offers the opportunity to generate such data without the need to create and observe the system in the real world. This is an attractive alternative in many cases. For example, building several possible versions of the real-world system might be impossible, impractical, or cost-prohibitive. In extreme cases (as in major construction), it might be true that no real version of the system exists. Some versions of the system might provide such poor service (as in medical settings) as to create unacceptable legal liabilities. In these and many other cases, discrete event simulation is clearly preferable to other approaches (e.g., direct observation).